Back

Engineering S. cerevisiae extracellular vesicles using synthetic biology

Bouffard, J.; Trani, J.; Pawelczak, A. C.; Laufens, M.; Nunez Soto, M.; Brett, C. L.

2026-03-06 synthetic biology
10.64898/2026.03.06.710173 bioRxiv
Show abstract

Extracellular vesicles (EVs) hold great promise as therapeutic delivery vehicles, leveraging their natural role as mediators of intercellular communication in all organisms studied. However, many barriers must be overcome to realize their full potential. Saccharomyces cerevisiae is an attractive chassis organism to explore solutions: It is used for drug biomanufacturing, it is amenable to complex genetic engineering, and their EVs can drive responses in human cells. To further develop this prospect, we sought to genetically modify S. cerevisiae EVs by devising a research framework amenable to iterative design, build, test, learn cycles - a core principle of synthetic biology. Using this approach, we focused on identifying new scaffolds - proteins that load cargoes into EVs - from a small pool of candidates. We first optimized a modular cloning strategy, called "EVclo", for plasmid and genome-integrated candidate gene expression. Candidate genes were fused to EGFP, and after confirming expression in cells, we showed that scaffold-EFGP proteins colocalized with mRuby2-tagged Nhx1, a biomarker of multivesicular bodies, presumed sites of EV biogenesis. We triggered release of EVs by heat stress, isolated these EVs by ultrafiltration and size exclusion chromatography, and confirmed the presence of exosome-sized EVs in all samples. We find that candidate scaffold proteins did not affect EV size, morphology or titers. Further analysis of these samples indicated that some EGFP-tagged scaffolds are present in EVs: Bro1, a yeast ortholog of ALIX, was most abundant and ExoSignal showed highest enrichment of the human candidates. In all, we conclude that Bro1 is a good scaffold for future engineering strategies, and that human proteins can be sorted into yeast EVs suggesting conservation of the sorting machinery and demonstrating that yeast EVs can be humanized. This synthetic biology-based, proof-of-concept study establishes S. cerevisiae as a platform to engineer and bioproduce designer EVs for many applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=167 HEIGHT=200 SRC="FIGDIR/small/710173v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1cf61dcorg.highwire.dtl.DTLVardef@21f412org.highwire.dtl.DTLVardef@11ecde9org.highwire.dtl.DTLVardef@160b3f7_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTS AND TOC BLURBO_LIsynthetic biology-based system was optimized to engineer EVs in S. cerevisiae C_LIO_LIEV scaffolds can be sorted to yeast EVs C_LIO_LIis an efficient scaffold to sort proteins into yeast EVs C_LIO_LIS. cerevisiae can be used to engineer designer EVs for drug delivery C_LI Extracellular vesicles (EVs) are a promising new modality for drug delivery. However, designer EVs must be engineered to broaden applications and improve efficacy. Here, Bouffard et al. optimize methods rooted in synthetic biology to genetically engineer EVs in S. cerevisiae, a yeast commonly used to manufacture biological drugs. They find that ectopically expressed human EV scaffolds (CD63, ExoSignal, PDGFR) can be sorted to yeast EVs, but Bro1 - the yeast ortholog of ALIX - was most efficient at sorting GFP into EVs. This proof-of-concept study demonstrates a single DBTL (design-build-test-learn) cycle that can be used to develop designer EVs for therapeutic applications.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
ACS Synthetic Biology
256 papers in training set
Top 0.2%
22.2%
2
Nature Communications
4913 papers in training set
Top 19%
9.9%
3
Advanced Science
249 papers in training set
Top 3%
6.3%
4
Journal of Controlled Release
39 papers in training set
Top 0.2%
4.8%
5
Angewandte Chemie International Edition
81 papers in training set
Top 0.9%
3.6%
6
Journal of the American Chemical Society
199 papers in training set
Top 2%
3.5%
50% of probability mass above
7
Advanced Biology
29 papers in training set
Top 0.1%
3.5%
8
Metabolic Engineering
68 papers in training set
Top 0.3%
3.0%
9
Small
70 papers in training set
Top 0.2%
2.7%
10
Chemical Science
71 papers in training set
Top 0.6%
2.3%
11
ACS Applied Bio Materials
21 papers in training set
Top 0.2%
2.0%
12
Advanced Healthcare Materials
71 papers in training set
Top 0.9%
1.9%
13
Nano Letters
63 papers in training set
Top 1%
1.8%
14
Biotechnology and Bioengineering
49 papers in training set
Top 0.5%
1.6%
15
Advanced Materials
53 papers in training set
Top 1%
1.6%
16
ACS Nano
99 papers in training set
Top 3%
1.5%
17
Communications Chemistry
39 papers in training set
Top 0.4%
1.5%
18
ACS Chemical Biology
150 papers in training set
Top 1%
1.3%
19
Nature Chemical Biology
104 papers in training set
Top 2%
1.2%
20
Nature Biotechnology
147 papers in training set
Top 6%
1.2%
21
ACS Central Science
66 papers in training set
Top 2%
0.9%
22
Angewandte Chemie
12 papers in training set
Top 0.2%
0.8%
23
eLife
5422 papers in training set
Top 56%
0.8%
24
Computational and Structural Biotechnology Journal
216 papers in training set
Top 10%
0.7%
25
Science Advances
1098 papers in training set
Top 30%
0.7%
26
ACS Omega
90 papers in training set
Top 4%
0.7%
27
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 3%
0.7%
28
Cell Reports Methods
141 papers in training set
Top 6%
0.6%